Name
Abstract | ||
|---|---|---|
Network embedding aims to map a complex network into a low-dimensional vector space while maximally preserving the properties of the original network. An attributed network is a typical real-world network that models the relationships and attributes of real-world entities. Its analysis is of great significance in many applications. However, most such networks are incomplete with partially-known attributes, links and labels. Traditional network embedding methods are designed for a complete network and cannot be applied to a network with incomplete information. Thus, this work proposes an inductive embedding model to learn the robust representations for a partially-unseen attributed network. It is designed based on a multi-core convolutional neural network and a semi-supervised learning mechanism, which can preserve the properties of such a network and generate the effective representations for unseen nodes in a model training process. We evaluate its performance on the task of inductive node classification and community detection via three real-world attributed networks. Experimental results show that it significantly outperforms the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TNSE.2020.3048902 | IEEE Transactions on Network Science and Engineering |
Keywords | DocType | Volume |
Attributed network,convolutional neural network,deep learning,inductive learning,network embedding | Journal | 8 |
Issue | ISSN | Citations |
1 | 2327-4697 | 2 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongying Zhao | 1 | 140 | 19.02 |
| Hui Zhou | 2 | 2 | 0.35 |
| Liang Qi | 3 | 156 | 27.14 |
| Liang Chang | 4 | 118 | 34.68 |
| Mengchu Zhou | 5 | 2 | 0.35 |